Identify what the extra eight GB change
Start from a specific case that fails or requires compromises on 24 GB: reduced sequence length, minimal batch size or components moved off the GPU. Replay that case with the same recipe on 32 GB and record memory after loading and at peak. You will then know whether the extra capacity solves the problem without changing the method, or whether a 48 GB card remains more appropriate.
Quantize with a quality check
A quantization experiment must keep a reference version and an evaluation set. Compare the weight format, memory used, latency and errors on hard examples. A reduction in weight volume does not on its own predict the memory of the whole process, since caches and buffers remain present. Keep the input length, output length and concurrency identical across variants.
For fine-tuning, define the parameters actually being trained and test a save of the adapters from the very first run. The export must be reloadable with the corresponding base model.
Check a Blackwell-ready stack
The Blackwell generation warrants an explicit check of PyTorch, CUDA and every compiled extension. An environment that launches on an RTX 4090 does not prove that its kernels support the RTX 5090. Prepare a test of the core operations before running the full data. Choose the 4090 as your comparison if 24 GB is enough, or the RTX 6000 Ada if a 48 GB envelope is the priority.
Book to answer a measurable question
Three days can validate compatibility and the capacity gain. Seven days allow a quantization or adaptation study. Thirty days serve a campaign that is already stabilized, with results exported regularly. Choose the duration and quantity in the configurator, then enter your details. Crypto payment does not require a KYC check; after your transfer, the "I have paid" button leaves a report on the order.